Question 2 Below is a regression analysis for salary being p

Question 2)

Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,  age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of  expressing an employee’s salary, we do not want to have both used in the same regression.)

Plase interpret the findings. ( Note: technically we have one for each input variable.  Listing it this way to save space.)

Ho: The regression equation is not significant.

Ha: The regression equation is significant.

Ho: The regression coefficient for each variable is not significant

Ha: The regression coefficient for each variable is significant

Sal

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.991559074655531

R Square

0.983189398531733

Adjusted R Square

0.98084373321058

Standard Error

2.65759257261024

Observations

50

ANOVA

df

SS

MS

F

Significance F

Regression

6

17762.2996738743

2960.38327897905

419.151611129353

0.0000000000000000000000000000000000018121523852609

Residual

43

303.700326125705

7.06279828199313

Total

49

18066

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-1.74962121233985

3.61836765827431

-0.48353881572507

0.631166489854994

-9.04675504271989

5.54751261804019

-9.04675504271989

5.54751261804019

Midpoint

1.21670105053015

0.0319023509083532

38.1382881163022

0.0000000000000000000000000000000000866416336978111

1.15236382831625

1.28103827274406

1.15236382831625

1.28103827274406

Age

-0.00462801024512803

0.0651972120227729

-0.0709847875628716

0.943738987458078

-0.136110719142857

0.126854698652601

-0.136110719142857

0.126854698652601

Performace Rating

-0.0565964405497542

0.0344950678086454

-1.64071109712588

0.108153181882588

-0.126162374711284

0.0129694936117759

-0.126162374711284

0.0129694936117759

Service

-0.0425003573420269

0.0843369820842078

-0.503935003265728

0.616879351918047

-0.212582091217666

0.127581376533612

-0.212582091217666

0.127581376533612

Gender

2.42033721201279

0.860844317571592

2.81158528041454

0.00739661875026853

0.684279192016561

4.15639523200902

0.684279192016561

4.15639523200902

Degree

0.275533414317469

0.799802304805058

0.344501900859896

0.732148118953479

-1.33742165470733

1.88848848334226

-1.33742165470733

1.88848848334226

Note: Since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and can be used in multiple regression equations

A )

Interpretation:

What is the value of the F statistic:

What is the p-value associated with this value:

Is the p-value <0.05?

Do you reject or not reject the null hypothesis:

What does this decision mean for our equal pay question(Do M and F get paid equaly?:

B )

For each of the coefficients:

Intercept

Midpoint

Age

Perf. Rat.

Service

Gender

Degree

What is the coefficient\'s p-value for each of the variables:

Is the p-value < 0.05?

Do you reject or not reject each null hypothesis:

What are the coefficients for the significant variables?

Using only the significant variables, what is the equation? Salary =

Is gender a significant factor in salary:

If so, who gets paid more with all other things being equal?

How do we know?

	Regression Statistics
	Multiple R	0.991559074655531
	R Square	0.983189398531733
	Adjusted R Square	0.98084373321058
	Standard Error	2.65759257261024
	Observations	50

Solution